ExpLang: Improved Exploration and Exploitation in LLM Reasoning with On-Policy Thinking Language Selection
Paper · 2026 · Changjiang Gao, Zixian Huang, Kaichen Yang et al.
Summary
Reasoning models are usually post-trained with RL to think in English, even though sampling chains of thought in several languages can yield more varied solutions and higher Pass@k. ExpLang turns the choice of thinking language into an action the model takes itself during RL. The pipeline has three steps:
- Qwen3-32B generates math reasoning traces in 12 non-English languages. These are filtered for language identity (LangDetect) and answer correctness (Math-Verify), and each trace is tagged with a language-selection marker.
- Qwen3-4B is cold-started on this data via LoRA SFT, which limits forgetting.
- GRPO-based RLVR follows in two phases. First, a diversity reward pushes the model to try rarely chosen languages. Then a language-level Pass@k reward favors languages that tend to produce correct answers.
With the same training budget, ExpLang consistently beats English-only GRPO on math benchmarks. It follows a requested thinking language almost perfectly for seen languages and generalizes to unseen ones. Multilinguality thus serves both as a performance lever and as support for users who want native-language reasoning.
Key points
- Training data: about 500 filtered traces per language, covering 12 languages (6 European, 6 Asian). Inputs are sampled from an English math training set, and each trace carries a
<lang_select>[code]</lang_select>tag placed before the thinking section. - RL schedule:
- Exploration (first 1/4 of steps): diversity reward weight λ_d = 0.2, with r_d = k_min/k, and the KL loss switched off.
- Exploitation (remaining 3/4): Pass@k-style reward weight λ_p = 0.5, applied across natural language sub-groups, with the KL loss switched on.
- Evaluation setup: benchmarks include MATH-500, AIME-2025 and OlymMATH. Generation is capped at 4096 tokens, so absolute scores fall below official Qwen3 figures. Language compliance in the ablations is around 99.7–99.9%.
- Analysis:
- Untrained Qwen3-4B already gets higher accuracy with non-English thinking than with English on 21.62% of 2000 training samples.
- During exploration, policy entropy stays higher than the baseline's; during exploitation it drops faster.
- The model's language preference moves from English to multilingual and back to English.
- Availability and scope: the method only adds batch-computed rewards, so it is compatible with GRPO variants such as Dr. GRPO, DAPO and GSPO. Code and data are at github.com/RiverGao/ExpLang.
Institutions
Fields, topics and concepts
📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works
🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 works
Cite
Cite as Changjiang Gao, Zixian Huang, Kaichen Yang, Jiajun Chen, Jixing Li and Shujian Huang. 2026. ExpLang: Improved Exploration and Exploitation in LLM Reasoning with On-Policy Thinking Language Selection. arXiv:2602.21887.
@misc{gao2026explang,
title = {{ExpLang: Improved Exploration and Exploitation in LLM Reasoning with On-Policy Thinking Language Selection}},
author = {Changjiang Gao, Zixian Huang, Kaichen Yang, Jiajun Chen, Jixing Li and Shujian Huang},
year = {2026},
doi = {10.48550/arxiv.2602.21887},
eprint = {2602.21887},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2602.21887}
}References
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